MS Research Proposal Presentation
•Transferir como PPTX, PDF•
3 gostaram•1,254 visualizações
Master's research proposal presentation to the department of Horticulture and Crop Science at The Ohio State University. Abstract: Chile peppers (Capsicum annuum), which grow in southern Mexico on a environmental gradient from warm and humid coastal areas to the cool, dry highlands, present a unique opportunity to study the range of environmental tolerance and adaptation. Understanding how chile peppers have adapted to local conditions will provide insight into the importance of specific environmental factors in organizing diversity across the landscape, and highlight traits with potential for future crop improvement. Over recent years, our international research team has sampled more than 200 plants from wild, semi-wild and domesticated populations across southern Mexico. Seed from these original collections will undergo one generation of increase in the greenhouse to eliminate maternal environmental effects in seeds used for planned phenotyping experiments. Genome-wide genotyping (GBS) will be conducted on these parent plants. I will conduct two experiments aimed at assessing short-term and long-term resistance to abiotic stress. I will study short-term resistance to drought and heat stress in seedlings by overlaying factorial environmental treatments (simulating the interaction between cool highland/warm lowland temperatures and moist coastal/drier inland environments of Oaxaca, Mexico) onto chile pepper accessions from our collection. I will assess long-term (i.e. full life cycle) drought resistance by comparing the effect of a field capacity treatment with an empirically determined water stress treatment across accessions in factorial combination. Habitat drought stress indices based on the Thornthwaite potential evapotranspiration (PET) model and the Hamon estimator will be assessed as drought resistance predictors. Using a genome wide association study (GWAS) approach, I will identify significant associations between genetic markers and observed values of gas exchange, as well as plant morphology, growth characteristics and overall fitness. Information gathered through this study will provide evidence for the genetic basis of both adaptive variation and phenotypic plasticity, therefore furthering the understanding of genetic diversity in chile peppers.
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Destaque
Destaque (20)
Semelhante a MS Research Proposal Presentation
Semelhante a MS Research Proposal Presentation (20)
Último
Último (20)
MS Research Proposal Presentation
- 1. Exploring climate adaptations in chile peppers (Capsicum annuum) of Southern Mexico Vivian Bernau 6 March 2015 Horticulture and Crop Science Colloquium MS Proposal Image:ChilePepperInstitute 1
- 2. Outline • Overview of heat and drought stress • Capsicum annuum: study site and germplasm • Objective 1: Estimating drought tolerance with PET modeling • Objective 2: Assessing phenotypic responses to drought and heat stress – Short-term heat and drought stress in seedlings – Long-term accumulated drought stress • Objective 3: Genome Wide Association Study Image:ChilePepperInstitute 2
- 3. Abiotic Stress and Plants TEMPERATURE STRESS • Denature proteins • Disrupt membrane lipids • Inactivate chloroplast enzymes • Speed up development • Increase transpiration WATER STRESS • Halt growth • Stimulate root growth • Foliage wilts • Stomata close Image:ChilePepperInstitute 3(Chaves et al. 2001; Wahid et al. 2007)
- 4. Drought Resistance Drought Avoidance Expressed in the absence of drought • Enhanced water uptake • Reduced water loss • Stomatal structure Drought Tolerance Triggered by drought • Osmotic adjustment • Antioxidant capacity • Desiccation tolerance Image:ChilePepperInstitute 4
- 5. Capsicum Center of Diversity Image:ChilePepperInstitute 5 (Pickersgill 1971)
- 6. Capsicum Center of Diversity Image:ChilePepperInstitute 6 (Kraft et al. 2014)
- 7. Image:ChilePepperInstitute 8 CAPS Chile Collections 2013-2014 Collection gradients: geographic, ethnic, domestication, climatic, altitudinal Collections by altitude (masl)
- 8. Temperature distribution of collection sites Mean temperature Maximum monthly temperature Minimum monthly temperature (Hijmans et al. 2005) Image:ChilePepperInstitute TemperatureC*10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
- 9. Distribution of precipitation at collection sites Image:ChilePepperInstitute 10 (Hijmans et al. 2005) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Precipitation(mm)
- 10. Bioclimatic Variables Image:ChilePepperInstitute 11 Bioclimatic Variables bio1 Annual Mean Temperature bio2 Mean Diurnal Range bio4 Temperature Seasonality bio5 Max Temperature of Warmest Month bio6 Min Temperature of Coldest Month bio8 Mean Temperature of Wettest Quarter bio9 Mean Temperature of Driest Quarter bio10 Mean Temperature of Warmest Quarter bio11 Mean Temperature of Coldest Quarter bio12 Annual Precipitation bio13 Precipitation of Wettest Month bio14 Precipitation of Driest Month bio15 Precipitation Seasonality bio16 Precipitation of Wettest Quarter bio17 Precipitation of Driest Quarter bio18 Precipitation of Warmest Quarter bio19 Precipitation of Coldest Quarter alt Altitude (Hijmans et al. 2005)
- 11. Principle Component Analysis Image:ChilePepperInstitute 12 • Component 1: temperature and altitude (bio1, bio5-11, alt) • Component 2: precipitation seasonality, precipitation of driest month and driest quarter (bio14, bio15, bio17) Western Coast Central Valleys Coast Yucatan
- 12. OBJECTIVE 1 13 Image:ChilePepperInstitute Estimating drought tolerance with PET modeling
- 13. Objective 1: Predicting drought tolerance Image:ChilePepperInstitute 14 Assessed faba bean germplasm for drought avoidance traits based on precipitation at point of origin. Introduced PET modeling to distinguish environmental variability
- 14. Objective 1: Predicting drought tolerance Image:ChilePepperInstitute 15 Calculating Drought Indices (DIs) Potential Evapotranspiration (PET) • Thornthwaite Method -- f(temperature + radiation) • Hamon Method -- f(temperature)
- 15. Objective 1: Predicting drought tolerance Hypotheses: • Significant differences between DIs of populations which clustered together in PCA. • Thornthwaite DI will correlate with performance in the long-term phenotyping study • Hamon DI will correlate with performance inthe short- term phenotyping study Image:ChilePepperInstitute 16 Methodology and Analysis: • Conducted in R • Machine learning analysis
- 16. OBJECTIVE 2 17 Image:ChilePepperInstitute Assessing phenotypic responses to drought and heat stress
- 17. Seed Increase from Original Collections 1. Seed for phenotyping studies – Maternal environmental effects limited 2. Tissue for DNA extraction Image:ChilePepperInstitute 18 Lines Plants in Greenhouse (194) Accessions Collected Seed (105) Populations Landrace (85) Farm (59) Location (28) Añil Farm 1 Costeño Rojo Plant 1 Line 1 Plant 2 Line 1 Line 2 Costeño Amarillo Plant 1 Line 1 Line 2 Farm 2
- 18. Rep1 Objective 2: Assessing Phenotypes Short-term assessment (seedling) • Conducted in growth chambers • Split-split plot design • Temperature x Water x Line (factorial) – 25/23˚C (control), 35/33˚C (stress) – Well watered, cease watering for 10 days – Line (105 accessions) Image:ChilePepperInstitute 19 2 – WW 5 – WW 3 – WW 4 – WW 4 – NoW 1 – NoW 5 – NoW 3 – NoW 3 – WW 3 – NoW 1 – WW 2 – WW 5– NoW 2 – NoW 4 – NoW 5 – WW 1 – WW 4 – WW 2 – NoW 1 – NoW
- 19. Objective 2: Assessing Phenotypes Short-term assessment (seedling) • Conducted in growth chambers • Split-split plot design • Temperature x Water x Line (factorial) – 25/23˚C (control), 35/33˚C (stress) – Well watered, cease watering for 10 days – Line (105 accessions) • Measure before and after stress treatment – Gas exchange – Stomatal conductance • Record final plant height, stem diameter, and aboveground dry matter Image:ChilePepperInstitute 20
- 20. Rep2 Rep1 Objective 2: Assessing Phenotypes Image:ChilePepperInstitute 21 2 – WW 5 – WW 3 – WW 4 – WW 4 – Stress 1 – Stress 5 – Stress 3 – Stress 3 – WW 3 – Stress 1 – WW 2 – WW 5– Stress 2 – Stress 4 – Stress 5 – WW 1 – WW 4 – WW 2 – Stress 1 – Stress Long-term assessment (full life-cycle) • Split-plot design • Factorial design with two factors – Line (105 accessions) – Water (applied by drip irrigation) • Field capacity (control) • 30% of field capacity (preliminary trial underway)
- 21. Objective 2: Assessing Phenotypes Long-term assessment (full life-cycle) • Split-plot design • Factorial design with two factors – Line (105 accessions) – Water (applied by drip irrigation) • Field capacity (control) • 30% of field capacity (preliminary trial underway) • Measured periodically – Gas exchange – Stomatal conductance – Relative leaf water content (RWC%) • Recorded fitness characteristics – Flowers produced – Fruit set Image:ChilePepperInstitute 22
- 22. Objective 2: Assessing Phenotypes • Quantitative data analyzed by ANOVA Hypotheses: • Lines originating from areas with higher precipitation and lower temperatures will have higher susceptibility to drought and heat stress • The combination of heat and drought stress will compound plant responses • Some lines will be resistant to short-term stress but not long-term stress and vice versa. Image:ChilePepperInstitute 23
- 23. OBJECTIVE 3 24 Image:ChilePepperInstitute Genotyping-by-Sequencing & Genome-Wide Association Study
- 24. Objective 3: GBS & GWAS Image:ChilePepperInstitute 25 Genotyping-by-Sequencing (GBS) • Collect tissue samples • Genomic DNA obtained using Qiagen DNeasy Plant Mini Kits • Sequencing and SNP Calling at Cornell University Genome-Wide Association Study (GWAS) GAPIT (Lipka et al. 2012) Many genes w/ small effects MLMM (Segura et a. 2012) Fewer genes w/ big effects
- 25. Objective 3: GBS & GWAS Hypotheses: • Using mixed linear models, we will identify DNA sequence variation(s) associated with: – short-term drought resistance – short-term heat stress resistance – long-term drought resistance • Short-term and long-term stress resistance will be associated with different loci. Image:ChilePepperInstitute 26
- 26. Overarching Implications • Improved understanding of genome • Provide a genetic basis for local adaptation • Insight for future conservation efforts Image:ChilePepperInstitute 27
- 27. Acknowledgements • Advisors – Dr. Leah McHale – Dr. Kristin Mercer • Committee Members – Dr. Peter Curtis – Dr. Lev Jardón Barbolla • CAPS PlantDom Team – Nathan Taitano – Rachel Capouya • Jim Vent • Mercer Lab • McHale Lab Image:ChilePepperInstitute 28
- 28. References Chaves, M M. 2002. “How Plants Cope with Water Stress in the Field? Photosynthesis and Growth.” Annals of Botany 89 (7): 907–16. doi:10.1093/aob/mcf105. Cortés, Andrés J, Fredy a Monserrate, Julián Ramírez-Villegas, Santiago Madriñán, and Matthew W Blair. 2013. “Drought Tolerance in Wild Plant Populations: The Case of Common Beans (Phaseolus Vulgaris L.).” PloS One 8 (5): e62898–e62898. doi:10.1371/journal.pone.0062898. Khazaei, Hamid, Kenneth Street, Abdallah Bari, Michael Mackay, and Frederick L. Stoddard. 2013. “The FIGS (Focused Identification of Germplasm Strategy) Approach Identifies Traits Related to Drought Adaptation in Vicia Faba Genetic Resources.” PLoS ONE 8 (5): e63107. doi:10.1371/journal.pone.0063107. Kraft, Kraig H., Cecil H Brown, Gary Paul Nabhan, Eike Luedeling, José De Jesús Luna Ruiz, Geo Coppens d’Eeckenbrugge, Robert J. Hijmans, and Paul Gepts. 2014. “Multiple Lines of Evidence for the Origin of Domesticated Chili Pepper, Capsicum Annuum, in Mexico.” Proceedings of the National Academy of Sciences 111 (17): 1–6. doi:10.1073/pnas.1308933111. Lipka, Alexander E., Feng Tian, Qishan Wang, Jason Peiffer, Meng Li, Peter J. Bradbury, Michael A. Gore, Edward S. Buckler, and Zhiwu Zhang. 2012. “GAPIT: Genome Association and Prediction Integrated Tool.” Bioinformatics 28 (18): 2397–99. doi:10.1093/bioinformatics/bts444. Pickersgill, Barbara. 1971. “Relationships between Weedy and Cultivated Forms in Some Species of Chili Peppers (Genus Capsicum.” Evolution 25 (4): 683–91. Segura, Vincent, Bjarni J. Vilhjálmsson, Alexander Platt, Arthur Korte, Ümit Seren, Quan Long, and Magnus Nordborg. 2012. “An Efficient Multi-Locus Mixed-Model Approach for Genome-Wide Association Studies in Structured Populations.” Nature Genetics 44 (7): 825–30. doi:10.1038/ng.2314. Thornthwaite, C W, and J R Mather. 1955. “The Water Balance.” Publications in Climatology 8 (1): 1– 104. Wahid, A., S. Gelani, M. Ashraf, and M. R. Foolad. 2007. “Heat Tolerance in Plants: An Overview.” Environmental and Experimental Botany 61 (3): 199–223. doi:10.1016/j.envexpbot.2007.05.011. 29
Notas do Editor
- Thank you all for joining me today as I present my Master’s research proposal. I’ll be talking about my research in chile peppers of Southern Mexico—exploring adaptations to climatic factors, specifically heat and drought stress.
- As my proposed research includes analysis of heat and drought stress, I’ll be beginning with a review of what that looks like in plants, before moving on to a background of the germplasm used in this study and where it comes from. From there I’ll jump into each of my objectives with a little bit of an introduction for each one. I have three main objectives: Estimating drought tolerance using potential evapotranspiration calculations Assessing phenotypic responses to drought and heat stress Genotyping the accessions and incorporating the phenotypic data in a genome-wide association study.
- The two main climatic stressors I’ll be investigating are temperature and water stress, or heat and drought stress. Water stress will cause plants to stop growing, or will stimulate root growth initially in order to search for more water. Foliage wilts as the plant’s water content decreases, and eventually stomata will close, ceasing gas exchange and carbon accumulation. Heat stress can cause plant proteins to denature, it can disrupt the structure of lipids in cell membranes or the composition of leaf cuticles. Heat stress can also inactivate enzymes in the chloroplast, More visual disruptions to normal function include speeding up plant development and increased transpiration—leading to increased water usage, compounding the effects of drought stress.
- Drought resistance is comprised of drought avoidance and drought tolerance. Drought avoidance mechanisms improve the plant performance under water stress conditions without leveraging yield. Examples include enhanced water uptake through the development of deep root systems or reduced water loss due to evapotranspiration. Drought tolerance mechanisms are triggered by drought. Osmotic adjustment is a response that triggers the accumulation of osmotically active compounds in the plant such as amino acids and sugars to lower osmotic potential and maintain turgor under low water conditions. Desication tolerance is achieved by stabilizing cell membranes so they can survive under dehydrated conditions without leaking electrolytes.
- The native distribution of the genus stretches from the southern United States through Central and South America to southern Brazil and Bolivia.
- But the center of origin is in Mexico. For some time there has been contestation about where in Mexico. Research published last year integrating paleobiolinguistics (the study of ancient languages), archaelogical data, and genetic data suggests that the center of origin is somewhere near the Tehuacan Valley. This is important because the center of origin is where the widest range of diversity is likely to be found. The accessions that I propose to study more in depth come from two states of Mexico: Oaxaca, located just to the south of the Tehuacan Valley, and Yucatan, located on the northern side of the Yucatan Peninsula.
- Our germplasm collections are particularly interesting because they were specifically selected across different geographic, climatic, ethnic and domestication gradients. Fr Wild plants were collected from forests, and cultivated peppers were collected from backyard gardens and mechanized plantations. The collection areas encompassed several indigenous cultures, and includes chile fruits used both fresh and dried. Most importantly for this study are the climatic gradients. In Oaxaca we have collections from the Central Valleys– a higher elevation compared with collections all along the coast. The coastal regions tend to be significantly hotter and wetter than the inland central valleys. The Yucatan tends to be even hotter and wetter than the coastal areas of Oaxaca.
- Here we have the temperature distribution of all of the accessions. The red indicates the maximum temperature of each location, the blue represents the minimum temperature for each location and the green represents the average. A pretty distinct dichotomy is visible distinguishing the coastal/yucatan locations from the cooler central valley locations. Note that all temperature ranges, including that of the maximum are quite large—typically stretching 12-13˚C.
- Precipitation tells a similar story, with some accessions receiving nearly four times as much precipitation at the height of fruit expansion in September.
- The data from the previous two graphs comes from the worldclim dataset. This dataset uses monthly precipitation and temperature values (like the ones shown previously) to generate bioclimatic variables which are more biologically meaningful and useful in work such as ecological niche modeling, They represent annual trends, seasonal variability, and limiting environmental factors. I conducted PCA using these 19 variables plus altitude to paint a better picture of which climatic variables would explain the most variability between location points.
- Plotting the first two PC produced four clusters with a few outliers representing the cooler, higher central valleys left of the y-axis and the warmer, lowland locations to the right of the y-axis. The Yucatan accession receive much more rainfall, so they cluster far above the x-axis (closer to 6!) and the western coast accessions are a bit drier, so they cluster below the x-axis. The first two principle components explain about 70% of the model, which is a pretty large percentage. The separation of 2 distinct temperature/altitude groups and 3 precipitation groups provides good evidence that studying these accessions under simulated conditions will produce a wide range of responses.
- However, I want to dive a little deeper into what we can determine with bioclimatic data. Before I came to OSU I was working on a project at the International Center for Tropical Agriculture in Colombia. There we were utilizing bioclimatic data to predict the species distribution of hundreds of species important for crop improvement. The methodology I propose here kind of reverses that approach to ask “What can we predict about a collection of germplasm based on where it came from?” This question allows us to explore adaptive traits based on the premise that germplasm will reflect the selection pressures of its source environment.
- The Focused Identification of Germplasm Strategy was developed as a method to improve the use of germplasm, but I think it can also be used to inform our observations of differing responses. Khazaei et al divided over 400 accessions simply into a wet and dry group based on precipitation, and assessed them based on drought avoidance traits (did not trigger stress response), proving that the methodology could be applied to predicting drought resistance. Cortes et al did not implement a FIGS per se but conducted a more in depth drought resistance analysis using the same assumptions but improved methodology.
- Instead of dividing accessions solely on precipitation of origin, they predicted evapotranspiration for each location using two methods. The Thornthwaite Method predicts evapotranspiration based on temperature and radiation (a function of latitude) The Hamon method predicts solely based on temperature. The resulting PET value for each method was then compared that to average precipitation to create a drought index. Since the input data is the monthly mean temperature and the average monthly temperature, we produce a drought index of month j. I will perform these calculations in R, and would like to learn how to integrate a machine learning analysis for highlighting phenotypic traits that contributed most to determining drought index. This type of technique is more suited to data with non-linear relationships.
- My research hypotheses build off of the preliminary results from the PCA which showed that a large portion (~23%) of variation between environments is determined by low precipitation. Therefore, I predict that I will find significant differences between the Dis of coastal locations and central valley locations. Cortés et al found that the Thornthwaite DI correlated well with long-term drought resistance, while the Hamon DI correlated with short-term or sporadic drought resistance---which leads in to my second objective:
- My second objective is to assess the phenotypic responses of the germplasm to drought and heat stress. As I alluded to on the last slide, I’ll be simulating two types of environmental stress: short-term or sporadic drought stress will be simulated on seedlings and compounded with a factorial treatment of heat stress. The second study will assess long-term drought resistance by simulating accumulated drought stress throughout the life-cycle.
- Prior to beginning the phenotyping studies we must increase our seed stock of each accession. These plants will also be the source of tissue collections for genomic DNA extraction. Seed for 144 accessions was sowed in early November and 1-2 plants of 105 accessions were transplanted in early January of this year. Most of the lines are currently flowering and fruiting in Howlett greenhouse, and I hope to begin sowing seed for the phenotyping experiments in early May.
- The short-term drought x heat stress assessment will be conducted in growth chambers implementing a factorial split-split plot design. Seedlings will be direct-seeded in small pots and a drought treatment will be applied to half of the plants after 50% of the plants have developed their second set of true leaves.
- The short-term drought x heat stress assessment will be conducted in growth chambers implementing a factorial split-split plot design. Seedlings will be direct-seeded in small pots and a drought treatment will be applied to half of the plants after 50% of the plants have developed their second set of true leaves.
- The long-term drought assessment will be conducted in Howlett greenhouse implementing a split plot design. Seedlings will be propagated in plugs and transplanted to larger pots after about 6 weeks of growth. The drought treatment will then be applied through drip irrigation. Accumulated water stress will be measured by changes in gas exchange, stomatal conductance, and relative leaf water content. Overall fitness will be assessed by flowering and fruit set.
- The long-term drought assessment will be conducted in Howlett greenhouse implementing a split plot design. Seedlings will be propagated in plugs and transplanted to larger pots after about 6 weeks of growth. The drought treatment will then be applied through drip irrigation. Accumulated water stress will be measured by changes in gas exchange, stomatal conductance, and relative leaf water content. Overall fitness will be assessed by flowering and fruit set.
- All quantitative data will by analyzed by ANOVA. Again, my hypotheses build off of the assumption that plants are more adapted to their local environment, I hypothesize that lines originating from areas with higher precipitation and lower temperatures during the growing season will be more susceptible to drought. Additionally, the combination of heat and drought stress will compound the plant responses, further limiting survival. Lines originating from areas with lower annual precipitation will have higher resistance to long-term stress. And, finally, resistance to either stress treatment will not necessarily conform to resistance to the other.
- My third objective will incorporate phenotypic data collected from the simulated environment studies with genotyping by sequencing in a genome-wide association study. Basically, a GWAS study examines if genetic variation is associated with a particular trait.
- Tissue samples will be collected from plants grown from original seed. Then, genomic DNA will be isolated using Qiagen Dneasy Plant Mini kIts. The genomic DNA will be sent to Cornell University’s institute of biotechnology for sequencing using Illumina HiSeq 2000 and SNP calling. Based on other studies in solanaceous crops, we expect to find at least 10,000 SNPs (single nucleotide polymorphisms). GWAS will be performed using two analysis packages built for R: GAPIT (Genome Association and Prediction Integrated Tool) and MLMM (Multi-Locus Mixed Model) which have been used to accurately identify marker-trait associations and even the causal genes fro phenotypic variation. GAPIT: many genes with a small effect on one trait MLMM: small number of genes, each with a big effect, determining a trait
- Using GAPIT and MLMM we will identify sequence variations associated with the applied environmental stress treatments Short-term and long-term stress resistances will be associated with different loci Objective 3. Associate genetic markers with phenotypes Causal genetic variation underlies all traits; therefore a genome-wide association study (GWAS) approach will be utilized to identify patterns between phenotypic data collected in the drought and heat studies with variations in genotypic data. Hypothesis 3.1 Through the application of mixed linear models, which incorporate phenotypic traits and marker genotypes—including ancestral and recent relatedness—we will identify DNA sequence variation(s) associated with both short-term drought and heat stress resistance and long-term drought resistance. Hypothesis 3.2 Short-term and long-term stress resistance will be associated with different loci. Or are you specifically looking for relationships/correlations between trait variation and genetic variation? Be more specific.